library(here)
library(readxl)
library(dplyr)
library(BelgiumMaps.StatBel)
library(sf)
library(tmap)
library(leaflet)
library(leaflet.minicharts)
library(htmltools)
library(ggplot2)
library(stringr)
library(htmlwidgets)
library(here)

1 Read in dynam and spatial data

dynam.prov = read_excel(
    '20180425_dynam_be_jobdata_origineel.xlsx', 'Sheet2',
    skip = 1,  col_names = c(
      'province_lbl', 'province_nis_code', 
      'bruto_toename_absoluut', 'bruto_afname_absoluut', 'netto_absoluut', 'delme',
      'delmetoo', 'bruto_toename_graad', 'bruto_afname_graad', 'netto_graad', 'delmetootoo', 'werkzaamheidsgraad')) %>%
  select(-delme, -delmetoo, -delmetootoo)
dynam.prov
dynam.prov  <- dynam.prov %>% 
  mutate(
    province_lbl = case_when(
      province_lbl == 'Brussels Hoofdstedelijk Gewest' ~ 'Brussel',
      TRUE ~ province_lbl))
dynam.prov = dynam.prov %>%
  mutate(
    province_lbl_pct = str_c(province_lbl, '    ', str_sub(as.character(netto_graad*100),1,4), '%'))
province_lbl_ordered <- dynam.prov %>%
  arrange(netto_graad) %>% # rangschik procentuele of absolute groei
  # arrange(absoluut_netto) %>%
  pull(province_lbl_pct)
    
dynam.prov = dynam.prov %>%
    mutate(province_lbl_pct = factor(province_lbl_pct, levels = province_lbl_ordered))
dynam.prov = dynam.prov %>%
  mutate(
    netto_pct = netto_graad*100,
    bruto_toename_pct = bruto_toename_graad*100,
    bruto_afname_pct = bruto_afname_graad*100,
    bruto_afname_absoluut_neg = bruto_afname_absoluut*-1)
data('BE_ADMIN_PROVINCE') 
data("BE_ADMIN_REGION")
# convert to simple features dataset-structure
provinces = st_as_sf(BE_ADMIN_PROVINCE) 
regions = st_as_sf(BE_ADMIN_REGION) 
# Spatial object for Brussels-region is not included in provences, add from region
provinces = rbind(
  provinces %>% select(CD_PROV_REFNIS, TX_PROV_DESCR_NL),
  regions %>%
    filter(TX_RGN_DESCR_NL == "Brussels Hoofdstedelijk Gewest") %>%
    mutate(CD_RGN_REFNIS = '04000') %>%
    select(
      CD_PROV_REFNIS = CD_RGN_REFNIS,
      TX_PROV_DESCR_NL = TX_RGN_DESCR_NL))
provinces = provinces %>%
  mutate(CD_PROV_REFNIS = as.character(CD_PROV_REFNIS)) %>%
  left_join(dynam.prov, c('CD_PROV_REFNIS' = 'province_nis_code'))
# get centroid coordinates for each province to plot barchart there
provinces_coords = as_tibble(st_coordinates(st_centroid(provinces)))
names(provinces_coords) <- c('centroid_long', 'centroid_lat')
provinces <- bind_cols(provinces, provinces_coords)

2 Descriptive graphs

2.1 Overlapping barchart example

# Overlapping barcharts:
# http://stephanieevergreen.com/overlapping-bars/
# https://gist.github.com/hrbrmstr/035f998517de2384e9962cff7df874bd
# 
gg <- ggplot(data=dynam.prov, aes(y=province_lbl_pct, yend=province_lbl_pct))
gg <- gg + geom_segment(aes(x=bruto_afname_absoluut, xend=0, color="Jobverlies"), size=10)
gg <- gg + geom_segment(aes(x=bruto_toename_absoluut, xend=0, color="Jobgroei"), size=5)
gg <- gg + scale_x_continuous(labels=function(x) format(x, decimal.mark = ',', big.mark = ".", scientific = FALSE))
gg <- gg + scale_color_manual(name=NULL, values=c(Jobverlies="#bebebf", Jobgroei="#1074bc"))
gg <- gg + guides(color=guide_legend(keywidth=0, override.aes=list(size=4)))
gg <- gg + labs(
  x=NULL, y=NULL,
  title="Alle provincies kennen een netto jobaangroei, met de grootste\nprocentuele groei in Namen",
  subtitle = 'Procentuele en absolute jobevolutie per provincie, 2016-2017',
  caption="Bron: HIVA-KU Leuven | DynaM | dynamresearch.be")
gg <- gg + theme_minimal()
gg <- gg + theme(axis.text.x=element_text(margin=margin(t=0)))
gg <- gg + theme(axis.text.y=element_text(margin=margin(r=-10)))
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold"))
gg <- gg + theme(plot.margin=margin(20,20,20,20))
gg <- gg + theme(plot.caption=element_text(size=8, margin=margin(t=10, r=0)))
gg <- gg + theme(legend.position=c(0.8, 0.9))
gg <- gg + theme(legend.direction="vertical")
gg <- gg + theme(legend.background=element_rect(fill="white", color="white"))
gg

3 Static map visualisations

3.1 Inkleuren bruto toename

tm_shape(provinces) +
  tm_polygons(col = 'bruto_toename_pct', palette = 'Greens', border.col = 'white', title = 'Bruto jobtoename (%)')

3.2 Inkleuren bruto afname

tm_shape(provinces) +
  tm_polygons(col = 'bruto_afname_pct', palette = 'Reds', border.col = 'white', title = 'Bruto jobafname (%)')

3.3 Inkleuren netto evolutie

tm_shape(provinces) +
  tm_polygons(col = 'netto_pct', palette = 'Greens', border.col = 'white', title = 'Netto jobevolutie (%)')

3.4 Inkleuren bruto jobtoename + exact cijfer

tm_shape(provinces %>% mutate(bruto_toename_pct = round(bruto_toename_pct, 2))) +
  tm_polygons(col = 'bruto_toename_pct', palette = 'Greens', border.col = 'white', title = 'Bruto jobtoename (%)') +
  tm_text('bruto_toename_pct', size= .7)

3.5 Inkleuren bruto jobafname + exact cijfer

tm_shape(provinces %>% mutate(bruto_afname_pct = round(bruto_afname_pct, 2))) +
  tm_polygons(col = 'bruto_afname_pct', palette = 'Reds', border.col = 'white', title = 'Bruto jobafname (%)') +
  tm_text('bruto_afname_pct', size= .7)

3.6 Inkleuren netto evolutie + exact cijfer

tm_shape(provinces %>% mutate(netto_pct = round(netto_pct, 2))) +
  tm_polygons(col = 'netto_pct', palette = 'Greens', border.col = 'white', title = 'Netto jobevolutie (%)') +
  tm_text('netto_pct', size= .7)

4 Interactive map visualisations

4.1 Achtergrondkleur netto-evolutie (%) + cijfers in mouse-over popup

bins <- c(0.0, 0.5, 1, 1.5, 2.0, 2.5)
pal_greens_netto <- colorBin("Greens", domain = provinces$netto_pct, bins = bins)
provinces <- provinces %>%
  mutate(popup_lbl = str_glue("<h3>{province_lbl}</h3><br /><b>Jobtoename: </b>{netto_pct}"))
labels <- sprintf(
  "<strong>%s</strong><br/>Jobtoename: %1.2f%%<br/>Jobafname: %1.2f%%<br/>Netto evolutie: %1.2f%%<br/>",
  provinces$province_lbl, provinces$bruto_toename_pct, provinces$bruto_afname_pct, provinces$netto_pct
) %>% lapply(htmltools::HTML)
map_title <- tags$div(
  HTML('<b>Netto jobevolutie per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)  
m.dynam.prov.netto.hover <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_netto(netto_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Jobevolutie (%)',
    pal = pal_greens_netto, values = ~netto_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")
m.dynam.prov.netto.hover

4.2 Achtergrondkleur bruto-toename (%) + cijfers in mouse-over popup

bins <- c(4, 5, 6, 7, 8)
pal_greens_toename <- colorBin("Greens", domain = provinces$bruto_toename_pct, bins = bins)
map_title <- tags$div(
  HTML('<b>Bruto jobtoename per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)
m.dynam.prov.toename.hover <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_toename(bruto_toename_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Bruto jobtoename (%)',
    pal = pal_greens_toename, values = ~bruto_toename_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")
m.dynam.prov.toename.hover

4.3 Achtergrondkleur bruto-afname (%) + cijfers in mouse-over popup

bins <- c(4, 5, 6, 7, 8)
pal_greens_afname <- colorBin("Reds", domain = provinces$bruto_toename_pct, bins = bins)
map_title <- tags$div(
  HTML('<b>Bruto jobafname per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)
m.dynam.prov.afname.hover <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_afname(bruto_toename_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Bruto jobafname (%)',
    pal = pal_greens_afname, values = ~bruto_toename_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")
m.dynam.prov.afname.hover

4.4 Achtergrondkleur netto-evolutie (%) + cijfers in mouse-over popup + netto-cijfer weergeven

bins <- c(0.0, 0.5, 1, 1.5, 2.0, 2.5)
pal_greens_netto <- colorBin("Greens", domain = provinces$netto_pct, bins = bins)
provinces <- provinces %>%
  mutate(popup_lbl = str_glue("<h3>{province_lbl}</h3><br /><b>Jobtoename: </b>{netto_pct}"))
labels <- sprintf(
  "<strong>%s</strong><br/>Jobtoename: %1.2f%%<br/>Jobafname: %1.2f%%<br/>Netto evolutie: %1.2f%%<br/>",
  provinces$province_lbl, provinces$bruto_toename_pct, provinces$bruto_afname_pct, provinces$netto_pct
) %>% lapply(htmltools::HTML)
map_title <- tags$div(
  HTML('<b>Netto jobevolutie per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)  
m.dynam.prov.netto.hover.number <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_netto(netto_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Jobevolutie (%)',
    pal = pal_greens_netto, values = ~netto_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")
m.dynam.prov.netto.hover.number = m.dynam.prov.netto.hover.number %>%
  addLabelOnlyMarkers(lng = ~centroid_long, lat = ~centroid_lat, 
                      label =  ~as.character(round(netto_pct,2)), 
                      labelOptions = labelOptions(noHide = T, direction = 'top', textOnly = T))
m.dynam.prov.netto.hover.number

4.5 Achtergrond nettoevolutie + clickable barchart met absolute evolutie

pal <- colorBin("Greens", domain = dynam.prov$netto_pct)
d.abs = dynam.prov %>%
  select(
    'Jobgroei' = bruto_toename_absoluut,
    'Jobverlies' = bruto_afname_absoluut_neg,
    'Netto jobevolutie' = netto_absoluut)
d.pct = dynam.prov %>%
  mutate(
    bruto_toename_pct = round(bruto_toename_pct,2),
    bruto_afname_pct = round(bruto_afname_pct*-1,2),
    netto_pct = round(netto_pct,2)) %>%
select(
  'Jobgroei' = bruto_toename_pct,
  'Jobverlies' = bruto_afname_pct,
  'Netto jobevolutie' = netto_pct)
m.prov = leaflet(provinces, width = '100%') %>% 
  # add grey arrondissement polygons w/t white border
  addPolygons(
    weight = 2,
    opacity = 1,
    dashArray = "3",
    fillColor = ~pal(netto_pct), color = 'white') %>% 
  addLegend(pal = pal, values = ~netto_absoluut, opacity = 0.7,
            title = 'Netto jobevolutie (%)',
  position = "bottomright")
colors <- c("#7cae00", "#f8766d", "#c77cff")
m.prov = m.prov %>%
  addMinicharts(
    provinces_coords$centroid_long, provinces_coords$centroid_lat,
    chartdata = d.abs,
    colorPalette = colors,
    width = 45, height = 45)
map_title <- tags$div(
   HTML('<b>Bruto en netto jobevolutie per provincie, 2017-2018 (<a href="https://dynamresearch.be/">DynaM</a>)</b>')
 )  
m.prov = m.prov %>% 
  addControl(map_title, position = "bottomleft")
m.prov
m.dyam.bar.netto.pct = leaflet(provinces, width = '100%') %>% 
  # add grey arrondissement polygons w/t white border
  addPolygons(
    weight = 2,
    opacity = 1,
    dashArray = "3",
    fillColor = ~pal(netto_pct), color = 'white') %>% 
  addLegend(pal = pal, values = ~netto_pct, opacity = 0.7,
            title = 'Netto jobevolutie (%)',
  position = "bottomright")
colors <- c("#7cae00", "#f8766d", "#c77cff")
m.dyam.bar.netto.pct = m.dyam.bar.netto.pct %>%
  addMinicharts(
    provinces_coords$centroid_long, provinces_coords$centroid_lat,
    chartdata = d.pct,
    colorPalette = colors,
    width = 45, height = 45)
map_title <- tags$div(
   HTML('<b>Bruto en netto jobevolutie per provincie, 2017-2018 (<a href="https://dynamresearch.be/">DynaM</a>)</b>')
 )  
m.dyam.bar.netto.pct = m.dyam.bar.netto.pct %>% 
  addControl(map_title, position = "bottomleft")
m.dyam.bar.netto.pct
# saveWidget(
#   m.dynam.prov, file = here::here('dynam_jobevolution', 'dynam_netto_map_selfcontained.html'), 
#   selfcontained = TRUE,
#   background = 'white')
# 
# saveWidget(
#   m.dynam.prov, file = here::here('dynam_jobevolution', 'dynam_netto_map_notselfcontained.html'), 
#   selfcontained = FALSE,
#   background = 'white')
---
title: "Dynam jobevolutie kaartjes"
output: 
  html_notebook: 
    code_folding: hide
    number_sections: yes
    theme: lumen
    toc: yes
---

```{r, message=FALSE, warning=FALSE}
library(here)
library(readxl)
library(dplyr)
library(BelgiumMaps.StatBel)
library(sf)
library(tmap)
library(leaflet)
library(leaflet.minicharts)
library(htmltools)
library(ggplot2)
library(stringr)
library(htmlwidgets)
library(here)
```

# Read in dynam and spatial data

```{r}
dynam.prov = read_excel(
    '20180425_dynam_be_jobdata_origineel.xlsx', 'Sheet2',
    skip = 1,  col_names = c(
      'province_lbl', 'province_nis_code', 
      'bruto_toename_absoluut', 'bruto_afname_absoluut', 'netto_absoluut', 'delme',
      'delmetoo', 'bruto_toename_graad', 'bruto_afname_graad', 'netto_graad', 'delmetootoo', 'werkzaamheidsgraad')) %>%
  select(-delme, -delmetoo, -delmetootoo)
```

```{r}
dynam.prov
```

```{r, warning=FALSE, message=FALSE}
dynam.prov  <- dynam.prov %>% 
  mutate(
    province_lbl = case_when(
      province_lbl == 'Brussels Hoofdstedelijk Gewest' ~ 'Brussel',
      TRUE ~ province_lbl))

dynam.prov = dynam.prov %>%
  mutate(
    province_lbl_pct = str_c(province_lbl, '    ', str_sub(as.character(netto_graad*100),1,4), '%'))

province_lbl_ordered <- dynam.prov %>%
  arrange(netto_graad) %>% # rangschik procentuele of absolute groei
  # arrange(absoluut_netto) %>%
  pull(province_lbl_pct)

    
dynam.prov = dynam.prov %>%
    mutate(province_lbl_pct = factor(province_lbl_pct, levels = province_lbl_ordered))

dynam.prov = dynam.prov %>%
  mutate(
    netto_pct = netto_graad*100,
    bruto_toename_pct = bruto_toename_graad*100,
    bruto_afname_pct = bruto_afname_graad*100,
    bruto_afname_absoluut_neg = bruto_afname_absoluut*-1)

```


```{r}
data('BE_ADMIN_PROVINCE') 
data("BE_ADMIN_REGION")

# convert to simple features dataset-structure
provinces = st_as_sf(BE_ADMIN_PROVINCE) 
regions = st_as_sf(BE_ADMIN_REGION) 

# Spatial object for Brussels-region is not included in provences, add from region
provinces = rbind(
  provinces %>% select(CD_PROV_REFNIS, TX_PROV_DESCR_NL),
  regions %>%
    filter(TX_RGN_DESCR_NL == "Brussels Hoofdstedelijk Gewest") %>%
    mutate(CD_RGN_REFNIS = '04000') %>%
    select(
      CD_PROV_REFNIS = CD_RGN_REFNIS,
      TX_PROV_DESCR_NL = TX_RGN_DESCR_NL))
```

```{r}
provinces = provinces %>%
  mutate(CD_PROV_REFNIS = as.character(CD_PROV_REFNIS)) %>%
  left_join(dynam.prov, c('CD_PROV_REFNIS' = 'province_nis_code'))
```


```{r, warning=FALSE}
# get centroid coordinates for each province to plot barchart there
provinces_coords = as_tibble(st_coordinates(st_centroid(provinces)))
names(provinces_coords) <- c('centroid_long', 'centroid_lat')
provinces <- bind_cols(provinces, provinces_coords)
```

# Descriptive graphs

## Overlapping barchart example

```{r, fig.width=8, fig.height=6, fig.align='center'}
# Overlapping barcharts:
# http://stephanieevergreen.com/overlapping-bars/
# https://gist.github.com/hrbrmstr/035f998517de2384e9962cff7df874bd
# 

gg <- ggplot(data=dynam.prov, aes(y=province_lbl_pct, yend=province_lbl_pct))
gg <- gg + geom_segment(aes(x=bruto_afname_absoluut, xend=0, color="Jobverlies"), size=10)
gg <- gg + geom_segment(aes(x=bruto_toename_absoluut, xend=0, color="Jobgroei"), size=5)
gg <- gg + scale_x_continuous(labels=function(x) format(x, decimal.mark = ',', big.mark = ".", scientific = FALSE))
gg <- gg + scale_color_manual(name=NULL, values=c(Jobverlies="#bebebf", Jobgroei="#1074bc"))
gg <- gg + guides(color=guide_legend(keywidth=0, override.aes=list(size=4)))
gg <- gg + labs(
  x=NULL, y=NULL,
  title="Alle provincies kennen een netto jobaangroei, met de grootste\nprocentuele groei in Namen",
  subtitle = 'Procentuele en absolute jobevolutie per provincie, 2016-2017',
  caption="Bron: HIVA-KU Leuven | DynaM | dynamresearch.be")
gg <- gg + theme_minimal()
gg <- gg + theme(axis.text.x=element_text(margin=margin(t=0)))
gg <- gg + theme(axis.text.y=element_text(margin=margin(r=-10)))
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold"))
gg <- gg + theme(plot.margin=margin(20,20,20,20))
gg <- gg + theme(plot.caption=element_text(size=8, margin=margin(t=10, r=0)))
gg <- gg + theme(legend.position=c(0.8, 0.9))
gg <- gg + theme(legend.direction="vertical")
gg <- gg + theme(legend.background=element_rect(fill="white", color="white"))
gg
```

# Static map visualisations

## Inkleuren bruto toename

```{r}
tm_shape(provinces) +
  tm_polygons(col = 'bruto_toename_pct', palette = 'Greens', border.col = 'white', title = 'Bruto jobtoename (%)')
```

## Inkleuren bruto afname

```{r}
tm_shape(provinces) +
  tm_polygons(col = 'bruto_afname_pct', palette = 'Reds', border.col = 'white', title = 'Bruto jobafname (%)')
```

## Inkleuren netto evolutie

```{r}
tm_shape(provinces) +
  tm_polygons(col = 'netto_pct', palette = 'Greens', border.col = 'white', title = 'Netto jobevolutie (%)')
```

## Inkleuren bruto jobtoename + exact cijfer

```{r}
tm_shape(provinces %>% mutate(bruto_toename_pct = round(bruto_toename_pct, 2))) +
  tm_polygons(col = 'bruto_toename_pct', palette = 'Greens', border.col = 'white', title = 'Bruto jobtoename (%)') +
  tm_text('bruto_toename_pct', size= .7)
```

## Inkleuren bruto jobafname + exact cijfer

```{r}
tm_shape(provinces %>% mutate(bruto_afname_pct = round(bruto_afname_pct, 2))) +
  tm_polygons(col = 'bruto_afname_pct', palette = 'Reds', border.col = 'white', title = 'Bruto jobafname (%)') +
  tm_text('bruto_afname_pct', size= .7)
```

## Inkleuren netto evolutie + exact cijfer

```{r}
tm_shape(provinces %>% mutate(netto_pct = round(netto_pct, 2))) +
  tm_polygons(col = 'netto_pct', palette = 'Greens', border.col = 'white', title = 'Netto jobevolutie (%)') +
  tm_text('netto_pct', size= .7)
```

# Interactive map visualisations

## Achtergrondkleur netto-evolutie (%) + cijfers in mouse-over popup

```{r}
bins <- c(0.0, 0.5, 1, 1.5, 2.0, 2.5)
pal_greens_netto <- colorBin("Greens", domain = provinces$netto_pct, bins = bins)

provinces <- provinces %>%
  mutate(popup_lbl = str_glue("<h3>{province_lbl}</h3><br /><b>Jobtoename: </b>{netto_pct}"))

labels <- sprintf(
  "<strong>%s</strong><br/>Jobtoename: %1.2f%%<br/>Jobafname: %1.2f%%<br/>Netto evolutie: %1.2f%%<br/>",
  provinces$province_lbl, provinces$bruto_toename_pct, provinces$bruto_afname_pct, provinces$netto_pct
) %>% lapply(htmltools::HTML)

map_title <- tags$div(
  HTML('<b>Netto jobevolutie per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)  

m.dynam.prov.netto.hover <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_netto(netto_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Jobevolutie (%)',
    pal = pal_greens_netto, values = ~netto_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")

m.dynam.prov.netto.hover
```

## Achtergrondkleur bruto-toename (%) + cijfers in mouse-over popup

```{r}
bins <- c(4, 5, 6, 7, 8)
pal_greens_toename <- colorBin("Greens", domain = provinces$bruto_toename_pct, bins = bins)

map_title <- tags$div(
  HTML('<b>Bruto jobtoename per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)

m.dynam.prov.toename.hover <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_toename(bruto_toename_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Bruto jobtoename (%)',
    pal = pal_greens_toename, values = ~bruto_toename_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")

m.dynam.prov.toename.hover
```

## Achtergrondkleur bruto-afname (%) + cijfers in mouse-over popup

```{r}
bins <- c(4, 5, 6, 7, 8)
pal_greens_afname <- colorBin("Reds", domain = provinces$bruto_toename_pct, bins = bins)

map_title <- tags$div(
  HTML('<b>Bruto jobafname per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)

m.dynam.prov.afname.hover <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_afname(bruto_toename_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Bruto jobafname (%)',
    pal = pal_greens_afname, values = ~bruto_toename_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")

m.dynam.prov.afname.hover
```

## Achtergrondkleur netto-evolutie (%) + cijfers in mouse-over popup + netto-cijfer weergeven

```{r}
bins <- c(0.0, 0.5, 1, 1.5, 2.0, 2.5)
pal_greens_netto <- colorBin("Greens", domain = provinces$netto_pct, bins = bins)

provinces <- provinces %>%
  mutate(popup_lbl = str_glue("<h3>{province_lbl}</h3><br /><b>Jobtoename: </b>{netto_pct}"))

labels <- sprintf(
  "<strong>%s</strong><br/>Jobtoename: %1.2f%%<br/>Jobafname: %1.2f%%<br/>Netto evolutie: %1.2f%%<br/>",
  provinces$province_lbl, provinces$bruto_toename_pct, provinces$bruto_afname_pct, provinces$netto_pct
) %>% lapply(htmltools::HTML)

map_title <- tags$div(
  HTML('<b>Netto jobevolutie per provincie, 2015-2016 (<a href="https://dynamresearch.be/">dynaM</a>)</b>')
)  

m.dynam.prov.netto.hover.number <- leaflet(provinces) %>%
  # setView(-96, 37.8, 4) %>%
  addPolygons(
    fillColor = ~pal_greens_netto(netto_pct),
    weight = 2,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    highlight = highlightOptions(
      weight = 5,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.7,
      bringToFront = TRUE),
    label = labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>%
  addLegend(
    title = 'Jobevolutie (%)',
    pal = pal_greens_netto, values = ~netto_pct, opacity = 0.7,
            position = "topright") %>%
  addControl(map_title, position = "bottomleft")

m.dynam.prov.netto.hover.number = m.dynam.prov.netto.hover.number %>%
  addLabelOnlyMarkers(lng = ~centroid_long, lat = ~centroid_lat, 
                      label =  ~as.character(round(netto_pct,2)), 
                      labelOptions = labelOptions(noHide = T, direction = 'top', textOnly = T))

m.dynam.prov.netto.hover.number

```


## Achtergrond nettoevolutie + clickable barchart met absolute evolutie 

```{r}
pal <- colorBin("Greens", domain = dynam.prov$netto_pct)
```


```{r}
d.abs = dynam.prov %>%
  select(
    'Jobgroei' = bruto_toename_absoluut,
    'Jobverlies' = bruto_afname_absoluut_neg,
    'Netto jobevolutie' = netto_absoluut)

d.pct = dynam.prov %>%
  mutate(
    bruto_toename_pct = round(bruto_toename_pct,2),
    bruto_afname_pct = round(bruto_afname_pct*-1,2),
    netto_pct = round(netto_pct,2)) %>%
select(
  'Jobgroei' = bruto_toename_pct,
  'Jobverlies' = bruto_afname_pct,
  'Netto jobevolutie' = netto_pct)
```


```{r results="asis", echo=FALSE}
cat("
<style>
.leaflet-container {
    background: #FFF;
}
</style>
")
```

```{r}
m.prov = leaflet(provinces, width = '100%') %>% 
  # add grey arrondissement polygons w/t white border
  addPolygons(
    weight = 2,
    opacity = 1,
    dashArray = "3",
    fillColor = ~pal(netto_pct), color = 'white') %>% 
  addLegend(pal = pal, values = ~netto_absoluut, opacity = 0.7,
            title = 'Netto jobevolutie (%)',
  position = "bottomright")

colors <- c("#7cae00", "#f8766d", "#c77cff")
m.prov = m.prov %>%
  addMinicharts(
    provinces_coords$centroid_long, provinces_coords$centroid_lat,
    chartdata = d.abs,
    colorPalette = colors,
    width = 45, height = 45)

map_title <- tags$div(
   HTML('<b>Bruto en netto jobevolutie per provincie, 2017-2018 (<a href="https://dynamresearch.be/">DynaM</a>)</b>')
 )  

m.prov = m.prov %>% 
  addControl(map_title, position = "bottomleft")

m.prov

```


```{r}
m.dyam.bar.netto.pct = leaflet(provinces, width = '100%') %>% 
  # add grey arrondissement polygons w/t white border
  addPolygons(
    weight = 2,
    opacity = 1,
    dashArray = "3",
    fillColor = ~pal(netto_pct), color = 'white') %>% 
  addLegend(pal = pal, values = ~netto_pct, opacity = 0.7,
            title = 'Netto jobevolutie (%)',
  position = "bottomright")

colors <- c("#7cae00", "#f8766d", "#c77cff")
m.dyam.bar.netto.pct = m.dyam.bar.netto.pct %>%
  addMinicharts(
    provinces_coords$centroid_long, provinces_coords$centroid_lat,
    chartdata = d.pct,
    colorPalette = colors,
    width = 45, height = 45)

map_title <- tags$div(
   HTML('<b>Bruto en netto jobevolutie per provincie, 2017-2018 (<a href="https://dynamresearch.be/">DynaM</a>)</b>')
 )  

m.dyam.bar.netto.pct = m.dyam.bar.netto.pct %>% 
  addControl(map_title, position = "bottomleft")

m.dyam.bar.netto.pct

```

```{r}
# saveWidget(
#   m.dynam.prov, file = here::here('dynam_jobevolution', 'dynam_netto_map_selfcontained.html'), 
#   selfcontained = TRUE,
#   background = 'white')
# 
# saveWidget(
#   m.dynam.prov, file = here::here('dynam_jobevolution', 'dynam_netto_map_notselfcontained.html'), 
#   selfcontained = FALSE,
#   background = 'white')
```

